Modeling drifting snow in Antarctica with a regional climate model: 1. Methods and model evaluation
Bibliographic record
Abstract
To simulate the impact of drifting snow on the lower atmosphere, surface characteristics and surface mass balance (SMB) of the Antarctic ice sheet regional atmospheric climate model (RACMO2.1/ANT) with horizontal resolution of 27 km is coupled to a drifting snow routine and forced by ERA‐Interim fields at its lateral boundaries (1989–2009). This paper evaluates the near‐surface and drifting snow climate of RACMO2.1/ANT. Modeled near‐surface wind speed (squared correlation coefficient R 2 = 0.64) and temperature ( R 2 = 0.93) agree well with observations. Wind speed is underestimated in topographically complex areas, where observed wind speeds are locally very high (>20 m s −1 ). Temperature is underestimated in winter in coastal areas due to an underestimation of downward longwave radiation. Near‐surface temperature and wind speed are not significantly affected by the inclusion of drifting snow in the model. In contrast, relative humidity with respect to ice increases in regions with strong drifting snow and becomes more consistent with the observations. Drifting snow frequency is the only observable parameter to directly validate drifting snow results; therefore, we derived an empirical relation for fresh snow density, as a function of wind speed and temperature, which determines the threshold wind speed for drifting snow. Modeled drifting snow frequencies agree well with in situ measurements and novel estimates from remote sensing. Finally, we show that including drifting snow is essential to obtaining a realistic extent and spatial distribution of ablation (SMB < 0) areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".